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ARXIV:2604.04683 · HOMOMORPHIC ENCRYPTION OPTIMIZATION · SUBMITTED 07 APR · 20:14 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04683HOMOMORPHIC ENCRYPTION OPTIMIZATIONSUBMITTED 07 APR · 20:14 UTCFRESHNESS UNKNOWNKemal Mutluergil · Deniz Elbek · Kamer Kaya · Erkay Savaş · arXiv
Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead.
Opportunity summary
Pain Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead. For sparse-matrix vector multiplication, the widely used Halevi and Shoup (2014) scheme has a cost linear in the number of occupied cyclic…
Homomorphic encryption (HE) enables computation over encrypted data but incurs a substantial overhead. For sparse-matrix vector multiplication, the widely used Halevi and Shoup (2014) scheme has a cost linear in the number of occupied…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Homomorphic encryption (HE) enables computation over encrypted data but incurs a substantial overhead.
Homomorphic Encryption Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead.
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Paper Pack
10.48550/arXiv.2604.04683Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead.
Abstract
Homomorphic encryption (HE) enables computation over encrypted data but incurs a substantial overhead. For sparse-matrix vector multiplication, the widely used Halevi and Shoup (2014) scheme has a cost linear in the number of occupied cyclic diagonals, which may be many due to the irregular nonzero pattern of the matrix. In this work, we study how to permute the rows and columns of a sparse matrix so that its nonzeros are packed into as few cyclic diagonals as possible. We formalise this as the two-dimensional diagonal packing problem (2DPP), introduce the two-dimensional circular bandsize metric, and give an integer programming formulation that yields optimal solutions for small instances. For large matrices, we propose practical ordering heuristics that combine graph-based initial orderings - based on bandwidth reduction, anti-bandwidth maximisation, and spectral analysis - and an iterative-improvement-based optimization phase employing 2OPT and 3OPT swaps. We also introduce a dense row/column elimination strategy and an HE-aware cost model that quantifies the benefits of isolating dense structures. Experiments on 175 sparse matrices from the SuiteSparse collection show that our ordering-optimisation variants can reduce the diagonal count by $5.5\times$ on average ($45.6\times$ for one instance). In addition, the dense row/column elimination approach can be useful for cases where the proposed permutation techniques are not sufficient; for instance, in one case, the additional elimination helped to reduce the encrypted multiplication cost by $23.7\times$ whereas without elimination, the improvement was only $1.9\times$.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 0% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead. For sparse-matrix vector multiplication, the widely used Halevi and Shoup (2014) scheme has a cost linear in the number of occupied cyclic diagonals, which may be many d...
METHOD
Homomorphic encryption (HE) enables computation over encrypted data but incurs a substantial overhead. For sparse-matrix vector multiplication, the widely used Halevi and Shoup (2014) scheme has a cost linear in the number of occupied cyclic diagonals, which may be many due to t...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Homomorphic encryption (HE) enables computation over encrypted data but incurs a substantial overhead.
WHY NOW
Homomorphic Encryption Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead. For sparse-matrix vector multiplication, the widely used Halevi and Shoup (2014) scheme has a cost linear in the number of occupied cyclic diagonals, which may be many due to the irregular nonzero pattern of the matrix.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Homomorphic encryption (HE) enables computation over encrypted data but incurs a substantial overhead. For sparse-matrix vector multiplication, the widely used Halevi and Shoup (2014) scheme has a cost linear in the number of occupied cyclic diagonals, which may be many due to the irregular nonzero pattern of the matrix.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Homomorphic encryption (HE) enables computation over encrypted data but incurs a substantial overhead.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Homomorphic Encryption Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Optimizes sparse matrix structures for homomorphic encryption to significantly reduce computational overhead.
Segment
Homomorphic Encryption Optimization
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
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CITED BY
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Build readiness
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passport absent
unknown
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Artifact maturity
GitHub and Hugging Face maturity payloads
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unknown
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
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Buyer clarity
missing
Current read
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Defensibility
missing
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Regulatory load
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Current read
No regulatory classification is attached.
Evidence
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Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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